How can hierarchical clustering be used to uncover additional information from the Titanic dataset?
Hierarchical clustering is a powerful technique used in machine learning to uncover additional information from datasets. In the case of the Titanic dataset, hierarchical clustering can provide valuable insights into the underlying patterns and relationships among the passengers. To understand how hierarchical clustering can be applied to the Titanic dataset, let's first define what it
What is the difference between k-means and mean shift clustering algorithms?
The k-means and mean shift clustering algorithms are both widely used in the field of machine learning for clustering tasks. While they share the goal of grouping data points into clusters, they differ in their approaches and characteristics. K-means is a centroid-based clustering algorithm that aims to partition the data into k distinct clusters. It
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, K means with titanic dataset, Examination review
How do we compare the groups identified by the k-means algorithm with the "survived" column?
To compare the groups identified by the k-means algorithm with the "survived" column in the Titanic dataset, we need to evaluate the correspondence between the clustering results and the actual survival status of the passengers. This can be done by calculating various performance metrics, such as accuracy, precision, recall, and F1-score. These metrics provide insights
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, K means with titanic dataset, Examination review
How do we preprocess the Titanic dataset for k-means clustering?
To preprocess the Titanic dataset for k-means clustering, we need to perform several steps to ensure that the data is in a suitable format for the algorithm. Preprocessing involves handling missing values, encoding categorical variables, scaling numerical features, and removing outliers. In this answer, we will go through each of these steps in detail. 1.
What is clustering in machine learning and how does it work?
Clustering is a fundamental technique in machine learning that involves grouping similar data points together based on their intrinsic characteristics. It is commonly used to discover patterns, identify relationships, and gain insights from unlabeled datasets. In this answer, we will explore the concept of clustering, its purpose, and how it works, specifically focusing on the
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, K means with titanic dataset, Examination review